Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example
Populations and Evolution
2020-09-22 v1 Physics and Society
Abstract
During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.
Cite
@article{arxiv.2005.04937,
title = {Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example},
author = {Christopher E. Overton and Helena B. Stage and Shazaad Ahmad and Jacob Curran-Sebastian and Paul Dark and Rajenki Das and Elizabeth Fearon and Timothy Felton and Martyn Fyles and Nick Gent and Ian Hall and Thomas House and Hugo Lewkowicz and Xiaoxi Pang and Lorenzo Pellis and Robert Sawko and Andrew Ustianowski and Bindu Vekaria and Luke Webb},
journal= {arXiv preprint arXiv:2005.04937},
year = {2020}
}